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Revolutionizing Image Classification: Training Large Convolutional Neural Networks on the ImageNet Dataset

Marktechpost

Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. Training the network took five to six days, leveraging optimized GPU implementations of convolution operations to achieve state-of-the-art performance in object recognition tasks.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

Ho’s innovative approach has led to several groundbreaking achievements: Her team at Carnegie Mellon University was the first to apply 3D convolutional neural networks in astrophysics. She led the first effort to accelerate astrophysical simulations with deep learning. Ho’s contributions have not gone unnoticed.

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The Evolution of ImageNet and Its Applications

Viso.ai

2011 – A good ILSVRC image classification error rate is 25%. 2012 – A deep convolutional neural net called AlexNet achieves a 16% error rate. 2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks.

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Using JPEG Compression to Improve Neural Network Training

Unite.AI

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

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Top Computer Vision Papers of All Time (Updated 2024)

Viso.ai

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). They found that removing any convolutional layer (each containing less than 1% of the model’s parameters) resulted in inferior performance.

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Pascal VOC Dataset: A Technical Deep Dive (2024 Guide)

Viso.ai

VOC2011 PASCAL VOC challenge took a big step forward in 2011 with VOC2011. Deep Learning Approaches Convolutional Neural Networks (CNNs) : The CNNs including AlexNet , VGGNet , and ResNet helped solve computer vision problems by learning the hierarchal features directly from the Pascal VOC data.

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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.